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Data Science: Journal of Computing and Applied Informatics
Published by Universitas Sumatera Utara
ISSN : 25806769     EISSN : 2580829X     DOI : -
Core Subject : Science,
Computer Science & IT
Data Science: Journal of Computing and Applied Informatics (JoCAI) is a peer-reviewed biannual journal (January and July) published by TALENTA Publisher and organized by Faculty of Computer Science and Information Technology, Universitas Sumatera Utara (USU) as an open access journal. It welcomes full research articles in the field of Computing and Applied Informatics related to Data Science from the following subject area: Analytics, Artificial Intelligence, Bioinformatics, Big Data, Computational Linguistics, Cryptography, Data Mining, Data Warehouse, E-Commerce, E-Government, E-Health, Internet of Things, Information Theory, Information Security, Machine Learning, Multimedia & Image Processing, Software Engineering, Socio Informatics, and Wireless & Mobile Computing. ISSN (Print) : 2580-6769 ISSN (Online) : 2580-829X Each publication will contain 5 (five) manuscripts published online and printed. JoCAI strives to be a means of periodic, accredited, national scientific publications or reputable international publications through printed and online publications.
Arjuna Subject : -
Articles 86 Documents
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Time Series Forecasting of Global Price of Soybeans using a Hybrid SARIMA and NARNN Model: Time Series Forecasting of Global Price of Soybeans Chi, Yeong Nain
Data Science: Journal of Computing and Applied Informatics Vol. 5 No. 2 (2021): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v5.i2-5674

Abstract

Global price of soybeans has a big impact because of the trade war between the U.S. and China. Under this circumstance, price forecast is vital to facilitate efficient decisions and will play a major role in coordinating the supply and demand of soybeans globally. Hence, the primary purpose of this study was to demonstrate the role of time series models in predicting process using the time series data of monthly global price of soybeans from January 1990 to January 2021. The SARIMA and NARNN models are good at modelling linear and nonlinear problems for the time series, respectively. However, using the hybrid model, a combination of the SARIMA and NARNN models has both linear and nonlinear modelling capabilities, can be a better choice for modelling the time series. The comparative results revealed that the Hybrid-LM model with 8 neurons in the hidden layer and 3 time delays yielded higher accuracy than the NARNN-LM model with 8 neurons in the hidden layer and 3 time delays, and the SARIMA, ARIMA(0,1,3)(0,0,2)12, model, according to its lowest MSE in this study. Thus, this study may provide an integrated modelling approach as a decision-making supportive method for formulating price forecast of soybeans for the global soybean market.
Analyzing Main Topics Regarding The Electronic Information and Transaction Act in Instagram Using Latent Dirichlet Allocation Kresnawan, Hans; Felle, Sola Graciana; Mokay, Hanna Gloria; Rakhmawati, Nur Aini
Data Science: Journal of Computing and Applied Informatics Vol. 5 No. 2 (2021): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v5.i2-6125

Abstract

Indonesia is currently experiencing its fourth industrial revolution in the 21st century. With the introduction of the internet, Indonesia is expected to gain more than a hundred billion US Dollars and twenty-six million job openings by 2030. The rising usage of information technology prompts regulators to develop The Electronic Information Transaction Act to protect the populace from cybercrime. However, the law attracts numerous criticism due to its vague interpretation. This resulted in numerous arrests of innocents throughout Indonesia. Thus, the public is trying to voice their opinions on social media for the sake of preventing any more cases in the future. The usage of Latent Dirichlet Allocation could provide numerous benefits for this research. The separation between latent topics among random mixtures helps to identify the common ground and correlation between each post. These latent topics will be elaborated with a sample post to provide insights and expectations of the public towards the law.
A Web-Based Diabetes Prediction Application Using XGBoost Algorithm Herlambang Dwi Prasetyo; Pandu Ananto Hogantara; Ika Nurlaili Isnainiyah
Data Science: Journal of Computing and Applied Informatics Vol. 5 No. 2 (2021): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v5.i2-6290

Abstract

One of the diseases that is generally characterized by symptoms of an increase in glucose levels in the blood and is one of the body diseases classified as chronic is diabetes. Diabetes suffered by a person from time to time can cause serious damage to other organs such as blood vessels, kidneys, heart and nerves. Machine learning provides various data mining algorithms that can be used to assist medical experts. The accuracy of machine learning algorithms is a measure of the effectiveness of decision support systems. Prediction of diabetes can be seen from the patient's medical record data, therefore the author wants to create a diabetes prediction system independently through a website-based application system. This application system will be combined with data observation, namely the science of data mining using the XGBoost algorithm. The dataset is divided into training data by 80% and testing data by 20%. Before the data modeling was carried out, we carried out various parameter setting scenarios with the hope of evaluating and evaluating the implementation to be applied, the parameters we adjusted were colsample_bytree, gamma, learning_rate, max_depth, n_estimators, reg_alpha, reg_lambda, and subsample. After sharing the data and tuning parameters, the resulting model by applying the XGBoost algorithm has an accuracy of 74.67%, the resulting precision value is 57.40%, the resulting recall value is 65.94%, the resulting specificity value is 78, 50%.
MobileNets-V1 Architecture for Web Based Fish Image Classification Herlambang Duwi Prasetyo; Pandu Ananto Hogantara; Ika Nurlaili Isnainiyah
Data Science: Journal of Computing and Applied Informatics Vol. 5 No. 2 (2021): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v5.i2-6291

Abstract

Recently, the research study about fish identification become a very challenging to researchers. Climate and environmental changes have a major impact on fish species and their environment. To identify fish using manual process is time consuming and need effort to gather samples in different environment. The identification of fish species is performed by using feature extraction and a series of features. Generally, the characteristic is divided into two groups namely general characteristics and anatomical features. General characteristics is characteristic that can be seen directly without the aid of tools. The characteristics include color, texture, and fiber direction. Although, manual is performed by expert but is possible that identification is not accurate. Therefore, to overcome the problem, we create a web-based application for identifying fish by using image as input. We use 10 class data with 300 images for each class. Then, we split into training and testing with 80:20 ratio. The application was developed by using the MobileNets- V1 model. The proposed method has accuracy on 89 %, that obtain from training score is 91.04%, validation is 88,96%. This score is higher than other methods that used in this application. Total time for computation process is about 127 minutes.
Implementation of Moving Object Tracker System Mohanad Abdulhamid; Adam Olalo
Data Science: Journal of Computing and Applied Informatics Vol. 5 No. 2 (2021): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v5.i2-6450

Abstract

The field of computer vision is increasingly becoming an active area of research with tremendous efforts being put towards giving computers the capability of sight. As human beings we are able to see, distinguish between different objects based on their unique features and even trace their movements if they are within our view. For computers to really see they also need to have the capability of identifying different objects and equally track them. This paper focuses on that aspect of identifying objects which the user chooses; the object chosen is differentiated from other objects by comparison of pixel characteristics. The chosen object is then to be tracked with a bounding box for ease of identification of the object's location. A real time video feed captured by a web camera is to be utilized and it’s from this environment visible within the camera view that an object is to be selected and tracked. The scope of this paper mainly focuses on the development of a software application that will achieve real time object tracking. The software module will allow the user to identify the object of interest someone wishes to track, while the algorithm employed will enable noise and size filtering for ease of tracking of the object.
The Role of Social Media on Student Consumer Behavior in the City of Bandung Stephani Putri Kawidjaya; Fandi Ahmad; Ayudhini Azzahra Permatasari
Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 1 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v6.i1-5081

Abstract

The Covid-19 pandemic, which has had a significant impact, is no exception in Indonesia. This pandemic affects human interactions with one another and changes the economy quite significantly. Then there was a change in how to transact from what was previously offline to online and impacted changes in business processes. This research was conducted to see the influence of social media on the behavior of this research using a descriptive qualitative approach in which the researcher made observations through interviews, then observed the field and distributed questionnaires as well as several previous research studies, based on the data obtained, it was reprocessed into rigid information, from the results. Research shows that 85% of consumers come from adolescents aged 18-25 years, where this consumer segmentation has a big role because they actively use social media as a means or media, especially in terms of transactions in E-Commerce, so it does not rule out that shopping and online transactions are currently. This is proliferating in all corners of the world, especially in one of the big cities in Indonesia, namely the City of Bandung.
Predicting the Oil Investment Decision through Data Mining Empirical Evidence in Indonesia Oil Exploration Sector Harry Patria
Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 1 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v6.i1-7539

Abstract

Petroleum investment decision remains subject to economic and financial research for decades. Due to capital intensive and higher risk on oil exploration, the investment decision has become more important than ever before. This study aims to shed some light on this issue by conducting four machine learning algorithms to predict the decision applying the dataset from 2007-2019. This study includes the Decision Tree, Random Forest, Naïve Bayes, and Support Vector Machine. A comparative performance analysis is the illustrated using confusion matrix, Cohen’s Kappa value, and the accuracy of each model and Area under the ROC Curve. In this study, a machine learning approach was carried out on the oil exploration data. The findings demonstrate that Naïve Bayes has the most accurate performance for the classification (94.5%), followed by Decision Tree (92.9%), Random Forest (90.9%), and Support Vector Machine (89.6%). In practice, the selected Naïve Bayes model was applied to assess the decision using a new data test. The findings can diminish the subjective blindness and confirmation bias in the investment decision and bring about a reasonable and orderly exploration and development of petroleum reserves.
An Optimized Wireless Network Architecture for Department of Agriculture, Region 1 Goldara, Ruel; Patacsil, Joseph
Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 1 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v6.i1-7873

Abstract

In the emergence of the highly connected Internet of Things (IoT) and the rise of wireless network technologies, organizations are confronting expanded wireless network capability, stability, and reliability. Each organization that aims to manage wireless network services that customers and employees request must be robust, secured, and easy to manage, henceforth; optimized wireless network architecture ought to be a top priority. This study developed the optimized wireless network architecture for the Department of Agriculture, Region 1 to further provide a robust, more secure, and reliable wireless network design using a Prepare, Plan, Design, Implement, Operate and Optimize Methodology. The comparative and evaluative research design was used in determining the wireless network status of the current wireless network design of the Department of Agriculture, Region 1 into other agencies' wireless network designs and standards as well as the testing and implementation of the optimized wireless network architecture. Wireless network simulation and results of the wireless network design are interpreted using the Received Signal Strength Level (RSSL) and service coverage area. The testing of the optimized wireless network architecture shows that it is reliable, stable, robust, and secured and it is applicable in the agency.
Design of Informatic Logic Teaching Meterial Appication Using Ideal Problem Solving Based on Android Case Study in Universitas Pamulang Tita Puspitasari
Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 1 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v6.i1-7412

Abstract

Basic Physics is a part of physics that studies about objects in nature, the symptoms of natural events and the interactions of objects in nature. This symptom is initially what our senses experience. For example, sight finds optics or light, hearing finds learning about sound, heat can also be felt (felt). In defining a quantity in physics, there must be rules for calculating the quantity in question based on other measurable quantities. In Basic Physics, it is necessary to understand the flow of questions well to how to calculate and explain them. However, sometimes students do not understand this course due to weakness in calculations or lack of understanding of material concepts and the use of formulas. For this reason, it is necessary to have the latest learning methods through Android to improve student understanding. The purpose of this paper is to determine whether the use of the IDEAL problem solving model based on Android is effective, because based on several researches, iDEAL problem solving can improve students' process skills, increase motivation and improve problem solving abilities. The results of this research are in the form of an application that is used as one of the innovative and android-based learning media so that it is more practical to learn anywhere and anytime.
The Applied Aproach Impact Information Security For Government and Company (A Review) Febriana, Henny; Dewi Sartika Br Ginting; Fuzy Yustika Manik; Mahyuddin
Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 1 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v6.i1-7935

Abstract

The Government and industry are currently using information and communication technology a lot. The Government and companies in Indonesia have regulations on operational standards and management procedures for information and communication systems security at Government regarding implementing information system security. Information security is essential because there are many threats to information security. Information security in this era of Information and Communication Technology (ICT) is fundamental. Information Exchange Environment (IEE) vulnerabilities have increased as threats become more widespread and complex; therefore, information security has become a fundamental problem for businesses, organizations, and governments. So, in this paper, we will explain the review of the impact of Information Security For Government and companies in terms of threats and types of information security. Some security system methods are analyzed, among others—application Security, cloud security, Cryptography, security infrastructure, Incident response, and vulnerability management. The results of this review analysis, it known threat has several solutions, each depending on the type of threat for both the Government and the Company. Furthermore, Information Security is considered very important, especially for data stored in Government and companies.

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